Functional Connectivity and Power Spectral Density Analysis of EEG Signals in Meditation
Meditation, an old practice of many traditions, has been getting a lot of attention for its supposed ability to relax and relieve stress. However, the neurophysiological mechanisms behind these effects are relatively unexplored. In this survey paper we look at the current literature of EEG based analytical methodologies (Functional Connectivity, Power Spectral Density/PSD) to study the effects of meditation on cerebral activity and evaluate the trustworthiness of these studies. We describe a global view of the brain wave patterns associated with relaxation and cognitive functions, that we can characterize precisely in terms of mediated with meditation. The paper also presents the implementation of the Functional Connectivity metrics like, weighted Phase Lag Index (wPLI), and their evaluations to characterize the brain network coupling during meditative states. We then evaluate PSD analysis as a way to evaluate variations of brain wave power distribution during meditation. Despite the widespread availability of these technologies, current research is missing substantial detail, especially regarding the empirical application of sophisticated EEG methods to demonstrate the therapeutic effect of meditation as demonstrated with this survey. Although meditation has been found to have multiple benefits, little has been studied about the neurophysiology responsible for it. The purpose of this work is to propose an integrated framework for future research that integrates diverse methodologies to elucidate the scientific basis of meditation's effects. Yet the implications of such research could be profound in the arena of clinical practice, yet existing findings do not seem to be sufficiently rigorous. In addition, this discussion also addresses index terms such as meditation, EEG Analysis, Functional Connectivity, Power Spectral Density (PSD) and Neurophysiological Effects so that a clearer idea of the intricate connection between these diverse aspects can be had.
Authors
- Savita Verma Attri (ORCID: https://orcid.org/0000-0002-1783-9094)
- Rajashri Khanai (ORCID: https://orcid.org/0000-0002-5080-722X)
Institutions
- KLE Technological University (IN)
Publication Details
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1109/icsc64553.2025.10969058
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43